Apparatus and method for correcting susceptibility artefacts in a magnetic resonance image

ABSTRACT

An apparatus and method are provided for performing phase unwrapping for an acquired magnetic resonance (MR) image. The method includes modelling the MR phase in the MR image using a Markov random field (MRF) in which the true phase φ(t) and the wrapped phase φ(w) are modelled as random variables such that at voxel i of said MR image φ(t)(i)=φ(w)(i)+2πn(i), where n(i) is an unknown integer that needs to be estimated for each voxel i. The method further includes constructing a graph consisting of a set of vertices V and edges E and two special terminal vertices representing a source s and sink t, where there is a one-to-one correspondence between cuts on the graph and configurations of the MRF, a cut representing a partition of the vertices V into disjoint sets S and T such that sεS and tεT. The method further includes finding the minimum energy configuration, E(n(i)|φ(w)) of the MRF on the basis that the total cost of a given cut represents the energy of the corresponding MRF configuration, where the cost of a cut is the sum of all edges going from S to T across the cut boundary. The method further includes using the values of n(i) in the minimum energy configuration to perform the phase unwrapping from φ(w) to φ(t) for the MR image. A confidence may be computed for each voxel using dynamic graph cuts. The unwrapped phase from two MR images acquired at different times may be used to estimate a field map from the phase difference between the two MR images. The field map may be converted into a deformation field which is then used to initialise a non-rigid image registration of the acquired MR image against a reference image. The deformation field of the non-rigid registration is controlled to be smoother where the confidence is high.

FIELD OF THE INVENTION

The present invention relates to an apparatus and method for correctingsusceptibility artefacts in magnetic resonance (MR) imaging, and to anapparatus and method for performing phase unwrapping of an MR image foruse in such corrections.

BACKGROUND OF THE INVENTION 1. Introduction

Magnetic resonance imaging (MRI) is widely used in medicine and otherapplications for performing three-dimensional imaging, for example of ahuman body (or part of a human body). In some cases, the MRI imaging maybe performed in real-time to provide real-time (dynamic) visual feedbackto a surgeon during a medical procedure, such as neuro-surgery.

It is well-known that certain forms of MRI, such as echo planar imaging(EPI) are subject to susceptibility artefacts, inaccuracies in intensityand geometry caused by inhomogeneities in the magnetic field (sometimesreferred to as B₀ inhomogeneities). Such inhomogeneities may arise atthe transition between two forms of matter having different magneticproperties (magnetic susceptibility), for example at the transitionbetween the brain and surrounding air or bone. These inaccuracies canresult in anatomical features being incorrectly located in an image, inother words, the positions of such features may be distorted from theirtrue locations. This distortion can be very significant, for example, inreal-time MRI imaging for image-guided surgery, where it is extremelyimportant to know the precise location of a medical instrument inrespect to the different anatomical features—otherwise (for example) atreatment may be applied to the wrong location, or the medicalinstrument may inadvertently damage some other component of the body.

Existing techniques for addressing the problem of B₀ inhomogeneities aregenerally divided into two categories. A first category is based ineffect on image analysis, without consideration of the underlyingphysics. In this category, it is assumed that a prior image of therelevant region of the body is available which does not suffer from B₀inhomogeneities—such an image will therefore be undistorted. Such anundistorted image can be obtained from various MRI imaging techniques,for example, T1 images. A registration or mapping is then produced basedon identifying features in the (potentially) distorted image withsimilar looking features in the undistorted image. This then allows thedistorted image to be warped or transformed to coincide in positionalterms with the undistorted image. In other words, a given feature (X) inthe distorted image is identified with the same feature (X′) in theundistorted image. The distorted image is then transformed(un-distorted) so that X now becomes coincident with (or at least closeto) X′. This new positioning is assumed to represent the correct (true)location of the anatomical feature corresponding to X. One drawback withthis image intensity mapping is that it relies upon the image containingsharp (distinct), well-identified features. However, if no (or only afew) such features are present in the image, for example, the imageappears rather homogenous, then it is difficult to perform the desiredregistration and subsequent transformation. A further drawback is thatthis method relies upon the availability of an existing undistortedimage.

A second category of technique for addressing the problem of B₀inhomogeneities is more physics-based, in that it tries to calculate theactual distortions arising from the B₀ inhomogeneities, and then toremove (cancel out) these distortions. A MRI signal strength at anygiven can be represented as a complex quantity S=ke^((iθ)), where k isthe absolute magnitude or intensity of the signal, and θ is the phase.Although most MRI imaging is based on the intensity k, it is known touse the phase θ to generate a field map, which can then be used toremove from the image distortions arising from the B₀ inhomogeneities.One difficulty with this approach is that the generating the field mapuses the absolute or total phase, θ_(T), whereas the MRI imaging onlyproduces a residual phase θ_(R), where θ_(R)=θ_(T) modulo(2π). In otherwords, θ_(T)=θ_(R)+N·2π, where N is an integer that varies from onelocation to another. Accordingly, this approach tries to perform a phase“unwrapping” process, in effect, to determine the value of N across theimage, which then allows the total phase θ_(T) to be determined from theresidual phase θ_(R). Once the total phase has been obtained, the fieldmap and hence the B₀ inhomogeneities can be determined, and the imageundistorted as appropriate.

Some known systems use a combination of both the first (image-based) andsecond (filed map-based) techniques. For example, [14] describes anapproach in which a field map-based technique is used first to undistortan acquired image, and the undistorted image produced in this manner isthen registered, using an intensity based approach, to an earlier(prior) image which was obtained without distortion. Although such anapproach has been reasonably effective, the computational demands tendto be high, which means that it is difficult to support real-timeimage-assisted surgery (for example). Moreover, it continues to bedesirable to remove distortion due to B₀ inhomogeneities from MRI imagesas accurately as possible.

2. Review of Some Existing Work

Echo planar imaging (EPI) provides high temporal resolution and isroutinely used in functional magnetic resonance imaging (fMRI) anddiffusion weighted imaging (DWI) sequences. In recent years,interventional MRI (iMRI) is fast emerging as the preferred imagingchoice for image-guided neurosurgery. The relatively high spatialresolution, excellent soft tissue contrast and the lack of ionizingradiation makes iMRI an attractive imaging option for image-guidedinterventions. Furthermore, along with conventional structural imaging,current commercial iMRI scanners can also perform diffusion andfunctional imaging which allows for imaging of eloquent brain areas andcritical white matter tracts along with the surgical target areas.Modern interventional MRI scanners use EPI sequences to acquire DWIimages during neurosurgery, which can be then used for localisation ofcritical white matter tracts that lie close to the area of intervention.EPI performs fast imaging by sampling the entire frequency space of theselected slice with one excitation pulse and fast gradient blipping.However, this results in very low bandwidth in the phase encodingdirection, which makes EPI images highly susceptible to smallperturbations of the magnetic field, giving rise to various artefactsarising due to magnetic field inhomogeneities. The primary source ofthese so-called susceptibility artefacts is the difference in magneticsusceptibility between various tissues being imaged. In the context ofneuroimaging, this leads to severe geometric and intensity distortionsin areas like the brain stem, frontal and temporal lobes. Thedistortions are especially severe as the surgically resected cavitycontains air and induces high susceptibility differences leading tolarge distortions around the area of resection. Recent works have shownthat diffusion weighted MRI images along with structural images couldmore accurately localise brain structures of interest duringneurosurgical procedures [1], [2]. There is also an interest inperforming tractography on interventional DWI images to segment whitematter structures of interest [3]-[5]. Hence, it is important toaccurately compensate for susceptibility artefacts to be able to use EPIimages for effective neuronavigation.

Correction of susceptibility induced distortions in EPI images fallsunder two broad categories: field map estimation and non-linear imageregistration. The field map estimation approach is the estimation of B₀magnetic field inhomogeneity at every voxel from phase images acquiredat different echo times as shown in [6]-[8]. It was shown in [9] thatcorrection of susceptibility artefacts by field maps is not entirelyaccurate in regions of high field inhomogeneity. This is especiallycritical when correcting EPI images that are acquired usinginterventional MRI during a neurosurgical procedure. The area ofresection often lies in close proximity to critical white matter tractsand as the neurosurgical procedure progresses, information on the exactlocation of the tract is beneficial for surgical outcome. However it isexactly at the resection margin with the brain/air interface that the B₀magnetic field is most inhomogeneous and produces maximum geometric andintensity distortions.

A popular alternative to field maps is to use intensity based non-rigidimage registration techniques to register the distorted EPI image to ahigh resolution undistorted T1-weighted MRI [10]-[13]. However, the EPIimages acquired interventionally have low signal-to-noise ratio andsuffer from various artefacts which makes intensity based imageregistration challenging. A recent work [14], an extension of [15],proposed generation of field map estimates from structural images, whichwas then used to sample a non-uniform B-spline grid for an elasticregistration based correction step. However, this work is difficult toapply in the interventional setting due to the complex physicalenvironment around the resection area and the need for tissuesegmentation maps. Registration based approaches which requireacquisition of an additional EPI image have also been proposed [16],[17]. However, these approaches add to the scan time duringneurosurgery.

SUMMARY OF THE INVENTION

The invention is defined in the appended claims.

One aspect of the invention provides a method of performing phaseunwrapping for an acquired magnetic resonance (MR) image comprising:

modelling the MR phase in the MR image using a Markov random field (MRF)in which the true phase φ(t) and the wrapped phase φ(w) are modelled asrandom variables such that at voxel i of said MR imageφ(t)(i)=φ(w)(i)+2πn(i), where n(i) is an unknown integer that needs tobe estimated for each voxel i;

constructing a graph consisting of a set of vertices V and edges E andtwo special terminal vertices representing a source s and sink t, wherethere is a one-to-one correspondence between cuts on the graph andconfigurations of the MRF, a cut representing a partition of thevertices V into disjoint sets S and T such that sεS and tεT;

-   -   finding the minimum energy configuration, E(n(i)|φ(w)) of the        MRF on the basis that the total cost of a given cut represents        the energy of the corresponding MRF configuration, where the        cost of a cut is the sum of all edges going from S to T across        the cut boundary; and

using the values of n(i) in the minimum energy configuration to performthe phase unwrapping from φ(w) to φ(t) for the MR image.

In some embodiments, the MR phase is modelled as a piecewise smoothfunction where the smooth component is due to the inhomogeneities in thestatic MR field and the non-smooth component arises due to changes inmagnetic susceptibility from one tissue type to another. The spatialsmoothness may be enforced by modelling the true phase as the MRF and asmoothness model is incorporated by using a potential function. The truephase may be modelled as a six-neighbourhood pairwise MRF where a convexpairwise potential function is defined between immediately adjacentneighbouring voxels. The convex pairwise potential function may be sumof squared differences. A variety of other metrics could be used toenforce spatial smoothness.

In some embodiments, the method further comprises weighting voxelcontributions on the basis that phase measurements in low signal areasof the MR image tend to be less reliable. Accordingly, phasemeasurements in high signal areas of the MR image are assigned a higherweight that phase measurements in low signal areas of the MR image. Theweight assigned to a given voxel may be determined from the probabilitythat the contribution of noise to the phase of a given voxel is lessthan £ radians, where £ is set to be a small value greater than zero. Aregion in the MR image containing only air may be used to determine thenoise variance of the MR image, wherein said noise variance is then usedfor determining the weight to be assigned to a given voxel. A variety ofother techniques could be used for weighting based on signal strength.

In some embodiments, a confidence estimation is performed at each voxelfor the phase unwrapping. The confidence associated with a given labelfor a random variable may be estimated as the ratio of the min-marginalenergy associated with that labelling to the sum of the min-marginalenergies for that random variable for all possible labels, wherein themin-marginal is the minimum energy obtained when a random variable isconstrained to take a certain label and the minimisation is performedover all remaining variables. The confidence may be computed for eachvoxel using dynamic graph cuts. Using dynamic graph cuts in this mannergenerally comprises constraining a given MRF node to belong to thesource or sink by adding an infinite capacity edge between the given MRFnode and the respective terminal node, and computing the min-marginalenergies by optimizing the resulting MRF. A variety of other techniquescould be used for obtaining a confidence associated with the phaseunwrapping,

In some embodiments, the method further comprises using the unwrappedphase from two MR images or the unwrapped phase difference between thetwo MR images to estimate a field map. The field map converted into adeformation field (also referred to as a displacement field), which mayin turn be used to initialise a non-rigid image registration of theacquired MR image against a reference image. The deformation field forperforming the non-rigid image registration may be parameterised usingcubic B-splines, where the B-splines are constrained to move only in thephase encode direction. A similarity measure used for the non-rigidimage registration is based on local information which encodes spatialcontext by varying the contribution of voxels according to their spatialcoordinates. The non-rigid image registration may be represented as adiscrete set of displacements in the phase encode direction, and thenon-rigid image registration is modelled as a first-order Markov randomfield. The first derivative of the deformation field for the non-rigidimage registration may be penalised to ensure a smooth transformation. Avariety of other techniques could be used for performing the non-rigidimage registration.

Assuming that confidence values are available from the phase unwrapping,a penalty term for penalising the first derivative of the deformationfield in the non-rigid image registration may be modulated by theconfidence computed for each voxel, such that the penalty weight ishigher in regions of high confidence than in regions of low confidence.A variety of other techniques could be used for incorporating theconfidence values from the field map into the image registrationprocedure.

One significant benefit of using graph cuts for the phase unwrapping isthat they can be performed much more quickly than many existingtechniques. This facilitates the use of the present approach inintra-operative procedures, such as image-assisted surgery. Furthermore,because a confidence level can be assigned to different portions of thefield map, and this confidence level can then be taken into account fora subsequent image registration procedure, this can help to improve theoverall results in terms of the correction of susceptibility artefactsfor MR images.

Another aspect of the invention provides a method for correctingsusceptibility artefacts in an acquired magnetic resonance imagecomprising the steps of:

performing phase unwrapping for an acquired magnetic resonance (MR)image, including computing a confidence for the phase unwrapping foreach voxel of the acquired MR image;

generating a field map for the acquired magnetic resonance image fromthe unwrapped phase;

converting the field map to a deformation field; and

using the deformation field to initialise a non-rigid image registrationof the acquired MR image against a reference image, wherein thedeformation field of the non-rigid registration is controlled to besmoother where said confidence is high.

(The confidence may also be used for the initialisation of the non-imageregistration).

In some embodiments, performing phase unwrapping may involve the use ofgraph cuts as described above. However, other methods, such as a MonteCarlo Markov chain (MCMC), can also be used to perform the phaseunwrapping and to obtain an associated confidence level (although suchother methods will typically be slower than graph cuts). Likewise,generating the field map from the unwrapped phase, and using the fieldmap to initialise a non-rigid image registration may also be performedas described above, although a variety of other techniques could be usedfor these activities. For example, phase unwrapping and/or field mapestimation may be performed using methods like, but not limited to,Expectation Propagation, Variational Bayes, Markov Chain Monte Carlo(MCMC) and Hamiltonian MCMC simulations. Such method characterise theposterior distribution over the estimation parameters given the measuredphase images, and can all be used to estimate the confidence associatedwith the phase unwrapping and/or the estimated field map.

In some implementations, a per voxel confidence of the phase unwrappingis computed. The generation of the field map then includes computing aconfidence for each voxel of the field map using the confidence from thephase unwrapping, plus an estimate of uncertainty in the image (whichmay be determined, for example, using one of the above techniques). Inother words, the resulting confidence for the field map is high wherethe confidence in the phase unwrapping is high, but lowered if there ishigh image uncertainty for the relevant voxel. The resulting confidencefor the field map can then be used to control the smoothness of thedeformation field. Such an approach can therefore be regarded ascontrolling the smoothness of the deformation field based on theconfidence from the phase unwrapping, but also feeding in an estimate ofimage uncertainty for the field map to provide an further or enhancedcontrol of smoothness.

Another aspect of the invention provides an apparatus for implementingany method described above. In some cases, the apparatus may comprise ageneral purpose computer system having one or more processors thatexecutes computer program code which instruct the computer system toperform the specified method. Such computer program code can be suppliedon a non-transitory medium, such as an optical disk, flash memory, overa network from a hard disk drive, and so on. In some cases, theapparatus may have at least some specialised hardware, such as one ormore graphical processing units (GPUs) for executing computer programcode to perform at least some of the method. In some cases the apparatusmay be linked to or integrated into an MR image acquisition and/oranalysis system, for example to support, diffusion weighted andfunctional MRI, as well as image-assisted or image-guided surgery.

In some embodiments, the approach described herein combines field mapand image registration based correction approach in a unified scheme. Aphase unwrapping algorithm based on graph cuts can associate anuncertainty measure with the estimated field map. The deformation fieldgenerated from the field map correction step and the associatedconfidence (uncertainty) measure are used to initialize and adaptivelyguide a subsequent image registration step. Some embodiments of thisapproach therefore include: a phase unwrapping algorithm using dynamicgraph cuts that also determines the uncertainty associated withestimated solution; and a registration algorithm that can be adaptivelydriven using the confidence map estimated from the phase unwrapping stepto refine the results in low confidence areas. The approach describedherein may be used, for example, to correct susceptibility artefacts ininterventionally acquired echo planar imaging (EPI) images duringneurosurgery.

More generally, echo planar imaging is routinely used in diffusionweighted and functional MRI due to its rapid acquisition time. However,the long readout period makes it prone to geometric and intensitydistortions, collectively termed susceptibility artefacts. The use ofthese distorted images hampers the effectiveness of image-guided surgerysystems as critical white matter tracts and functionally eloquent brainareas cannot be accurately localised. The approach described hereinprovides, in some embodiments, a method for the correction ofdistortions arising from susceptibility artefacts in echo-planar MRIimages that combines field map and image registration based correctiontechniques in a unified framework. In some embodiments, a phaseunwrapping algorithm is utilised that can efficiently compute the B₀magnetic field inhomogeneity map as well as the uncertainty associatedwith the estimated solution through the use of dynamic graph cuts. Thisinformation may then used to adaptively drive a subsequent imageregistration step to further refine the results in areas with highuncertainty. The effectiveness of the unified algorithm described hereinin correcting for geometric distortions due to susceptibility artefactsis illustrated on interventionally acquired echo planar MRI images, asdiscussed in more detail below.

Accordingly, the approach described herein helps to correct robustly andaccurately for the geometric and intensity distortions in echo planarMRI images through field maps and image registration techniques. Fieldmaps are prone to be noisy, especially at the interface of issueboundaries with different magnetic susceptibilities. Dynamic graph cutsmay be used to find the most likely field map and uncertaintyinformation associated with the generation of the field map. Thisestimated uncertainty associated with the generation of the field mapmay be used for a subsequent image registration step that is selectivelydriven in regions of high uncertainty. This approach supports anefficient computational implementation and is suitable for use insituations where (near) real-time susceptibility correction is required,such as for MR images acquired during a neuro-surgical procedure tosupport surgical navigation.

Accordingly, the approach described herein can be used to employ twocomplementary techniques to correct more accurately for susceptibilityartefacts in EPI imaging. The initialisation from the field map baseddeformation, taken confidence into account, helps to produce a moreaccurate deformation field to correct for distortions. This approachcould be utilised, for example, in any image processing pipeline thatused EPI based MR images for analysis (EPI is the most frequently usedacquisition for diffusion and functional MRI). This approach could alsobe utilised, for example, in an image-guided neurosurgery setting inwhich EPI images are acquired pre- and intra-operatively to locate whitematter tracks. Without correct, the localisation can be quiteinaccurate, especially around areas of tissue resection. Correcting forthe susceptibility artefacts as described herein can help to lead tomore accurate navigation during neurosurgery.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention will now be described by way ofexample only with reference to the following drawings:

FIG. 1 is a schematic illustration depicting a graph having a sourcenode s and a sink node t, linked by a set of intervening vertices andedges, and with a cut through the graph to partition the nodes into tworegions, one including the source node s, and the other including thesink node t;

FIG. 2 presents some results obtained by phase unwrapping simulateddata, and compares the approach described herein against phaseunwrapping using a known technique and against a known (ground truth)image;

FIG. 2 presents some results obtained by phase unwrapping simulateddata, and compares the approach described herein against phaseunwrapping using a known technique and against a known (ground truth)image;

FIG. 3 presents some results obtained by correcting susceptibilityartefacts in a magnetic image using the approach described herein incomparison with using a known technique;

FIG. 4 is a high-level flowchart that provides an overview of processingaccording to one embodiment of the invention;

Table 1 presents some timing results for obtaining the results of FIG.2; and

Table 2 presents some data representing residual errors in tensor fitsassociated with the results presented in FIG. 3.

DETAILED DESCRIPTION 1. Methods A. Phase Modelling

A known method for estimating the field map is to use the phasedifference between two MR images acquired at different echo times.However, the phase images are uniquely defined only in the range of (−π,π] and hence the phase images need to be unwrapped at each voxel by anunknown integer multiple of 2π to obtain the true phase as in eq. (1).

φ_(t)(i)=φ_(w)(i)+2πn _(i)  (1)

where φ_(t)(i) is the true phase at a given voxel i, φ_(w)(i) is thewrapped phase and n is the unknown integer multiple of 2π that needs tobe estimated. Phase unwrapping is an ill-posed problem and becomesintractable without regularization.

Similar to [18], the phase unwrapping method can be modelled as a MarkovRandom Field (MRF) where the true phase φ(t) and the wrapped phase φ(w)are modelled as random variables. We aim to find the discrete label setn(i) that gives the maximum a posteriori (MAP) estimate of the phasewraps:

$\begin{matrix}{{\varphi_{t}(i)} = {\max \; \underset{\underset{Likelihood}{}}{P\left( \varphi_{w} \middle| \varphi_{t} \right)}\underset{\underset{Prior}{}}{P\left( \varphi_{t} \right)}}} & (2)\end{matrix}$

The likelihood term in eqn. (2) is modelled as δ((φ(w)−W(φ(t)), where δis the delta function and W(φ(t)) is the wrapped true phase. Thisproblem is ill-posed and additional constraints on the true phase areincorporated in terms of prior probabilities.

The MR phase can usually be modelled as a piecewise smooth functionwhere the smooth component is due to the inhomogeneities in the staticMR field and the non-smooth component arises due to changes in themagnetic susceptibility from one tissue type to another. The spatialsmoothness is enforced by modelling the true phase as a MRF andincorporating the smoothness model through a suitable potentialfunction. In this work, we model the true phase as a six-neighbourhoodpairwise MRF where the pairwise potential function used is the sum ofsquare of difference between adjacent neighbours. Owing to the MRF-Gibbsequivalance [19], the phase unwrapping problem is to find the MRFlabelling or configuration that minimises the energy E(k|φ(w)):

$\begin{matrix}{{E\left( k \middle| \varphi_{w} \right)} = {\underset{n}{argmin}{\sum\limits_{i \in I}{\sum\limits_{s \in N}{V\left( {\Delta \; \varphi_{t}} \right)}}}}} & (3)\end{matrix}$

where I are the image voxels, N is the set of neighbours for a givenvoxel at i, and V (Δφ_(l)) is a convex potential function defined on thedifference potential between a voxel at i and each neighbour (s) in N.The unknown integer wraps are denoted by k.

B. Energy Minimization Via Graph Cuts

As described herein, graph cuts are used for finding the minimum energyconfiguration of an MRF by constructing a graph where there isone-to-one correspondence between the cuts on the graph andconfigurations of the MRF. Additionally, the total cost of a given cutshould represent the energy of the corresponding MRF configuration.Hence, the task of finding the minimum energy configuration of the MRFin eqn. (3) is equivalent to finding the cut on the representative graphwith the minimum cost. The minimum cost cut can be efficiently found byusing the maximum flow algorithm [22].

Graph cuts have previously been used as a method for optimization ofmulti-label problems [20], [21]. A graph G=(V, E) consists of a set ofvertices V and edges E and two special terminal vertices termed source sand sink t. A cut on such a graph is a partition of the vertices V intodisjoint sets S and T such that sεS and tεT. The cost of the cut is thenthe sum of all edges going from S to T across the cut boundary. It isshown in [21] that an energy function with the form of eq. (4) is graphrepresentable as long as each pair-wise term E^(ij) satisfies theinequality in eq. (5). The energy function disclosed herein has thestructure of eq. (4) with a null unary data term.

$\begin{matrix}{{E\left( {x_{1},x_{2},{\ldots \mspace{14mu} x_{u}}} \right)} = {{\sum\limits_{i = 1}^{u}{E^{i}\left( x_{i} \right)}} + {\overset{u}{\sum\limits_{i = {1:{i < j}}}}{E^{ij}\left( {x_{i},x_{j}} \right)}}}} & (4) \\{{{E^{ij}\left( {0,0} \right)} + {E^{ij}\left( {1,1} \right)}} \leq {{E^{ij}\left( {0,1} \right)} + {E^{ij}\left( {1,0} \right)}}} & (5)\end{matrix}$

We now proceed to show that the pair-wise terms in our energy functionsatisfy the required inequality constraint. If the pairwise energy termV is convex and if the minimum of E(k|φ(w)) is not reached, there existsa binary image such that δε(0, 1) and E(k+δ|φ_(w))<E(k|φ_(w)). Assuminga two neighbourhood MRF system for the sake of brevity, letK_(t+1)=K_(t+)δ^(i) be the wrap count at time t+1 at voxel i. Then, wehave eq. (6) where Δφ_(t) is the difference in the true phase betweenthe MRF neighbours.

Δφ_(t)=2π(k _(t+1) ^(i) −k _(t+1) ^(i−1))+(φ_(w) ^(i)−φ_(w) ^(i−1))  (6)

After simple algebraic manipulation of eq. (6), we can rewrite theenergy function as eq. (7).

$\begin{matrix}{{E\left( {k^{t} + \delta} \middle| \varphi_{w} \right)} = {\underset{n}{argmin}{\sum\limits_{i \in I}{\sum\limits_{s \in N}{V\left( {{2{\pi \left( {\delta^{i} - \delta^{i - 1}} \right)}} + {2{\pi \left( {k_{t}^{i} - k_{t}^{i - 1}} \right)}} + \left( {\varphi_{w}^{i} - \varphi_{w}^{i - 1}} \right)} \right)}}}}} & (7)\end{matrix}$

Now considering the terms in eq. (5), we have:

E(0,0)=V(t)

E(1,1)=V(t)

E(0,1)=V(2π+t)

E(0,1)=V(−2π+t)

-   -   where        We can now see that E_(ij)(0, 0)+E_(ij)(1, 1)≦E_(ij)(0,        1)+E_(ij)(1, 0) or V(2π+t)+V(−2π+t)≧2×V(t) as V is convex.        Hence, the proposed energy term can be represented by a graph.

FIG. 1( a) shows how an elementary graph between two MRF neighbours isconstructed when E_(ij)(1,0)−E_(ij)(0,0)>0 andE_(ij)(1,0)−E_(ij)(1,1)>0. Similar constructions exist for other cases,and we refer the reader to [21] for more details. The complete graph isbuilt by merging the elementary graphs for each node pair as illustratedin FIG. 1( b). After the complete graph is built, the minimum cut on itcan be found by pushing the maximum flow between the source and sink.FIG. 1( c) illustrates that at each binary optimization step, theconfidence value can be calculated with each labelling. For example, theconfidence of being in state (1) after two binary optimizations is givenby σ¹(0,0)×σ²(0,1)+σ¹(0,1)×σ²(1,1).

C. Weighting Voxel Contributions

A quality map can be integrated into this optimization scheme toincrease its robustness. Phase measurements in low signal areas tend tobe less reliable and these areas can be discounted by assigning a weightto each voxel based on its magnitude. Similar to [23], the weight w_(v)at a voxel v is given by eq. (8) where |p| is the voxel magnitude, E isa small value greater than zero and erf is the error function. The noisevariance σ is computed using a manually selected region of interest inthe phase image containing only air.

$\begin{matrix}{w_{v} = {{erf}\left( \frac{{p}\tan \; \varepsilon}{\sigma \sqrt{2}} \right)}} & (8)\end{matrix}$

Equation (8) can be interpreted as the probability that the contributionof noise was less than E radians to the phase of a given voxel v.

D. Confidence Estimation in Phase Unwrapping

It is shown in [24] that the uncertainty associated with the MAPsolution can be estimated using Graph Cuts through computation ofmax-marginals. Max-marginals are a general notion and can be defined forany function as eq. (9). Hence, the max-marginal is the maximumprobability over all MRF configurations where an MRF site x_(v)

is constrained to takethe label j (x_(v)=j).

$\begin{matrix}{\mu_{v;j} = {\max\limits_{{x \in L},{x_{v} = j}}{\Pr (x)}}} & (9)\end{matrix}$

The max-marginals can be used to compute the confidence measureassociated with any random variable labelling as eq. (10):

$\begin{matrix}{\sigma_{v;j} = {\frac{\max\limits_{{x \in L},{x_{v} = j}}{\Pr (x)}}{\sum\limits_{k \in L}{\max\limits_{{x \in L},{x_{v} = k}}{\Pr (x)}}} = \frac{\mu_{v;j}}{\sum\limits_{k \in L}\mu_{v;k}}}} & (10)\end{matrix}$

Hence, the confidence σ_(v,j) for a random variable x_(v) to take thelabel j is given by the ratio of the max-marginal associated withassigning label j to variable x_(v) to the sum of max-marginals for allpossible label assignments for the variable x_(v). As shown in [24],this confidence can be expressed in terms of min-marginal energies,where a min-marginal is the minimum energy obtained when we constrain arandom variable to take a certain label and minimise over all theremaining variables as in eq. (11):

$\begin{matrix}{\psi_{v;j} = {\underset{{x \in L},{x_{v} = j}}{argmin}{E(x)}}} & (11)\end{matrix}$

By leveraging the MRF-Gibbs equivalence, the confidence measure can beexpressed in terms of the min-marginals as eq. (12). Hence, theconfidence associated with a given label for a random variable is theratio of the min-marginal energy associated with that labelling to thesum of min-marginal energies for that variable for all possible labels.We refer the reader to [24] for further details.

$\begin{matrix}{\sigma_{v;j} = \frac{\exp \left( {- \psi_{v;j}} \right)}{\sum\limits_{l \in L}{\exp \left( {- \psi_{v;l}} \right)}}} & (12)\end{matrix}$

Dynamic graph cuts can be used to compute σ_(v;j) for each voxel atevery binary optimization step in a very efficient manner. A given MRFnode can be constrained to belong to the source or the sink by adding aninfinite capacity edge between it and the respective terminal node. Noother changes need to be made to the graph and the required min-marginalcan be computed by optimizing the resulting MRF. Hence, to compute themin-marginals at every binary optimisation step, one has to optimise onesuch MRF for every node v and each of the two labels. Usually these MRFsare very close to each other and form a slowly varying dynamic MRFsystem, so that the search trees from previous computations can beefficiently reused, which greatly reduces the computation time.

E. Image Registration Framework

The displacement field and the confidence map generated from the phaseunwrapping step are used to initialize the subsequent non-rigidregistration step. The registration algorithm follows closely from [25],[26] and is formulated as a discrete multi-labelling problem. Thedeformation field is parameterised using cubic B-splines [27] and alocal variant of the normalized mutual information (SEMI) as describedby Zhuang et al. [28] is used as the similarity measure. SEMI is a localinformation measure and encodes the spatial context by varying thecontribution of the voxels according to their spatial coordinates. Underthis scheme, the joint histogram is computed as shown in eq. (13), whereIr(x) and I_(f)(x) are the reference and transformed floating images,w_(r) and w_(f) are Parzen windows functions, and the joint histogram iscalculated for the local region w_(s). In addition, W_(s)(x) is aweighting function for the spatial encoding and is a Gaussian kernelcentered on the control point.

Normalized mutual information [29] is computed for each of the localregions from these joint histograms. The local nature of the similaritymeasure allows the problem to be formulated in the MRF framework and canbe solved using the graph cuts framework.

$\begin{matrix}{{H_{s}\left( {r,f} \right)} = {\sum\limits_{x \in \omega_{s}}{\left( {w_{r}{I_{r}(x)}w_{f}{I_{f}(x)}} \right){W_{s}(x)}}}} & (13)\end{matrix}$

The geometric distortion due to susceptibility is dominant in the phaseencode (PE) direction; hence the B-spline control points are constrainedto move only in the PE direction. We consider a discrete set ofdisplacements in the PE direction and a label assignment to a controlpoint is associated with displacing the control point by thecorresponding displacement vector. This allows us to formulate theregistration as a discrete multilabel problem modeled in the first-orderMRF, where the B-spline control points are the random variables (MRFnodes) and the goal is to assign individual displacement values to thesenodes. Additionally, we penalize the first derivative of thedisplacement field to ensure a smooth transformation. Thisregularization can be represented as the pairwise binary terms, whereasthe similarity measure can be represented as the unary term.

We modulate the weight of the penalty term by the confidence mapobtained during the phase unwrapping step. In regions of highconfidence, we keep the relative weight of the penalty term high whereaswe relax it in areas with low confidence to allow for more displacement.Hence, a spatially varying cost function takes the form of eqn. (14)where σ_(i) is the confidence at voxel i and P_(i) is the global penaltyterm weight and ∥d∥ is the norm of the displacement between neighbouringcontrol points. The penalty term weights are initialized by projectingthe confidence map onto the control point grid. This cost function isoptimised using an alpha-expansion variant of the graph cutsminimisation algorithm [20].

$\begin{matrix}{C = {\sum\limits_{i \in}\left( {{\left( {1 - {\sigma_{i} \times P_{i}}} \right) \times {SEMI}_{i}} - \left( {\sigma_{i} \times P_{i} \times {d}} \right)} \right)}} & (14)\end{matrix}$

2. Validation Validation Using Simulated Data

We have validated the phase unwrapping algorithm using simulated phaseMRI data. To conduct the simulations, an MRI simulator software packagewas used: POSSUM (Physics Oriented Simulated Scanner for UnderstandingMRI) [30], [31]. The algorithm solves fundamental Bloch equations tomodel the behaviour of the magnetisation vector for each voxel of thebrain and for each tissue type independently. The signal coming from onevoxel is obtained by analytical integration of magnetisation over itsspatial extent, and the total signal is formed by numerically summingthe contributions from all the voxels. For a given brain phantom, pulsesequence and magnetic field values, POSSUM generates realistic MRimages. Magnetic field values are calculated by solving Maxwell'sequations which as an input use an air-tissue segmentation of the brain,and their respective susceptibility values. These magnetic field valuesare fed into the Bloch equation solver in POSSUM, resulting in imageswith extremely realistic susceptibility artefacts. A further, in-depth,description of POSSUM is presented in [30].

Here we use a 3D digital brain phantom from the MNI BrainWeb database,which is very thoroughly segmented into various tissues such as grey andwhite matter, cerebrospinal fluid (CSF), and with a very good air-tissuesegmentation [32]. We assume a 1.5T scanner, and use appropriate MRparameter values for white matter T1=833 ms, T2=83 ms, spin densityrho=0.86; grey matter T1=500 ms, T2=70 ms, rho=0.77 and CSF T1=2569 ms,T2=329 ms and rho=1. A typical field map sequence was acquired: twogradient echo images with different echo times (TE1=8 ms, TE2=10 ms).Spatial resolution was 2×2×2 mm and TR=700 ms.

In order to make the simulated images representative of images acquiredduring a surgical procedure, resections were introduced into the inputphantom. The resections were designed to match the typical resectionsmade during surgical treatment of refractory focal epilepsy. Hence,actual T1-weighted intra-operative scans were used as reference forlesion design. This modified phantom was used as an input to POSSUM andwrapped phase images and ground truth magnetic field values weresimulated.

Table 1 presents quantitative unwrapping results for the methoddescribed herein and PRELUDE, which is an existing two for performing 3Dphase unwrapping of images (seehttp:/fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/fugue/prelude.html). The resultswere compared with the original (ground truth) phase, and themisclassification ratio (MCR) was calculated. The MCR is the ratio ofthe number of voxels that were incorrectly unwrapped to the total numberof voxels. Both PRELUDE and the proposed unwrapping algorithm performcomparably well under low-noise conditions. However, at higher noiselevels the proposed algorithm outperforms PRELUDE both in terms of MCRand execution time. In addition, the method described herein alsogenerates the confidence associated with the unwrapping solution and cancompute it well within the time constraints associated with aneurosurgical procedure. Indeed, it is clear from Table 1 that themethod described herein is much faster than existing algorithms such asPRELUDE.

A visual example of using the phase unwrapping algorithm describedherein is shown in FIG. 2. In particular, FIG. 2( a) depicts a maskedslice through a noise free wrapped image; FIG. 2( b) is the same imageas FIG. 2( a) but corrupted with Gaussian noise. FIG. 2( c) shows theground truth unwrapped image and FIG. 2( d) shows the unwrapping resultobtained from PRELUDE. Some discontinuities around the lesion stillexist when unwrapping with PRELUDE (highlighted in red). FIG. 2( e)shows the unwrapped image using the phase unwrapping algorithm describedherein, with no phase discontinuities evident. FIG. 2( f) shows theconfidence map generated by the method described herein in conjunctionwith the unwrapped image of FIG. 2( e).

Validation Using Clinical Data

The method described herein was used on 13 datasets that were acquiredusing interventional MRI during temporal lobe resection procedures forsurgical management of temporal lobe epilepsy. The images were acquiredusing a 1.5T Espree MRI scanner (Siemens, Erlangen) designed forinterventional procedures. The T1-weighted MR image, used in theregistration step, was acquired with a resolution of 1.1×1.1×1.3 mmusing a 3D FLASH sequence with TR=5.25 ms and TE=2.5 ms. The EPI imagewas acquired using a single shot EPI with GRAPPA parallel imaging(acceleration factor of 2) and with a spatial resolution of 2.5×2.5×2.7mm.

There is no immediate way to validate the susceptibility correctionobtained using the described herein in the absence of a ground truthdeformation. One known approach for validating other correctiontechniques has been to identify landmarks on the EPI and T1- orT2-weighted MR images (obtained with conventional spin or gradient echosequences with negligible spatial distortion) and measure the distancebetween the landmarks before and after performing the correction.However, this approach tends to bias the results towards imageregistration based schemes. In addition, it is very difficult to pickreliable landmarks on interventionally acquired EPI images due toincreased levels of noise and deformation. Since we are especiallyinterested in achieving accurate artefact correction in the white matterareas, we focused on looking at the effect of susceptibility correctionon residual tensor fit errors. One potentially significant source oftensor fit errors are geometric distortions arising from susceptibilityartefacts. Therefore, accurate correction of susceptibility artefactsshould reduce residual errors after performing tensor fitting.

The diffusion tensors were reconstructed using dtifit [33] and sum ofsquare residual errors for the diffusion tensor fits were obtained forthe 13 subjects. For the validation, the initial sum of square residualtensor fit errors were computed for all subjects. Correction wasperformed after unwrapping the phase maps using PRELUDE and the phaseunwrapping algorithm described herein. The field maps generated usingboth of these methods were slightly smoothed using a low pass Gaussianfilter. We also performed the correction using only the registrationalgorithm described above (see section E above, image registrationframework), and also the method described herein based on a combinationof both the field map and image registration algorithm. The quantitativeresults from this analysis are presented in Table II. A paired t-testshowed that the method described herein showed a statisticallysignificant reduction (p-value <10⁻³) in residual tensor fit errors whencompared to field map and image registration based techniques alone.

FIG. 3 shows a representative slice obtained using the method describedherein. It will be seen that the corrected B₀ image shows a good visualcorrespondence with the undistorted T1-weighted image. Moreparticularly, FIG. 3 presents images shown the result of correcting forsusceptibility-induced spatial distortion using the method describedherein. FIG. 3( a) shows the gold-standard high resolution T1 imageacquired during surgery. FIG. 3( b) shows the uncorrected B₀ image witha large geometric distortion around the resected area. FIG. 3( c) showsthe result of correcting for susceptibility artefacts using the fieldmap estimation method described herein. FIG. 3( d) shows furtherimprovement of the result when the field map estimation is combined withthe image registration step described herein.

3. Flowchart

FIG. 4 is a flowchart which presents a high-level overview of theprocessing described herein in accordance with one embodiment of theinvention. The processing begins with acquiring one or more MR image(s)(operation 210). In particular, an MR image is acquired in a mode, suchas EPI, that suffers from susceptibility artefacts. Note that such an MRimage from an MR will usually comprise a pair of phase images or a phasedifference image (which is the difference in phase at two echo times).In addition, an MR image may also be acquired in a mode that does notsuffer from susceptibility artefacts—this can then be used as areference MR image (and will be referred to as such below). Such areference MR image may be obtained at an earlier time (e.g. not as partof the same procedure in which an EPI image is generated).

The acquired MR image that suffers from susceptibility artefacts thenundergoes a phase unwrapping procedure (operation 220). As describedabove, this phase unwrapping may be implemented by modelling the phaseusing a Markov random field, and constructing a graph corresponding tothe MRF. Dynamic graph cuts are then used to find the minimum energyconfiguration of the MRF, which in turn allows the phase unwrapped to bedetermined. A field map can be generated from the unwrapped phase, andthis can then be converted into a deformation field (operation 230) asper known techniques. The deformation field (also referred to as adisplacement field) is used to initialise a non-rigid image registrationprocedure as described above, which determines in effect a mapping orregistration between the acquired MR image and the reference MR image(operation 240). This mapping then allows the acquired MR image to beprocessed to remove the susceptibility artefacts, thereby generating acorrected MR image (operation 250).

In some embodiments, the non-rigid image registration of operation 240may be omitted. In other words, in such embodiments, the deformationfield from generated from the phase unwrapping can be used directly toproduce a corrected MR image. In general, the resulting correction forthis approach will not be as accurate as if the image registration ofoperation 240 is performed—however, such reduced performance may beacceptable (or unavoidable—for example, if no reference image isavailable for the image registration). Irrespective of whether the imageregistration of operation 240 is included, the use of dynamic graph cutsto perform the phase unwrapping allows the phase unwrapping to beperformed much more quickly than existing techniques—this may be ofparticular significance for use in (near) real-time environments, suchas surgical procedures.

In some embodiments, the phase unwrapping includes the production of aconfidence map, which indicates how accurate the phase unwrapping (andhence the resulting deformation field) is at any given location. Thisconfidence map can then be utilised during the image registration ofoperation 240 by constraining the deformation field more tightly whereconfidence in the phase unwrapping is high. The use of confidence fromthe phase unwrapping to help control the image registration has beenfound to help improve the overall correction obtained. Note that theconfidence map for the phase unwrapping can be efficiently produced viadynamic graph cuts as discussed above. Alternatively, other approachesto phase unwrapping, such as Monte Carlo Markov chain (MCMC), could alsobe employed to generate a confidence map for initialising an imageregistration procedure, although these other techniques will tend to beslower than the use of graph cuts.

4. Discussion

Accurate susceptibility correction is important to make effective use ofdiffusion imaging capabilities of interventional MRI. Susceptibilityartefacts are especially severe in the interventional setting due to thepresence of a resection cavity which induces large susceptibilitydifferences. The problem is further amplified by the fact that thewide-bore MR systems often used in intra-operative applications have asmaller uniform static magnetic field region than diagnostic scannersand the subject head usually does not experience a uniform magneticfield as there is limited flexibility with regards to the headplacement. This problem can be mitigated to a certain degree by fillingthe resection cavity with a saline solution, but this is not feasible ina majority of the interventions. The extent of distortion can also bereduced by the use of parallel imaging techniques [34] or segmented EPIacquisition [35]. However, this is not always feasible and does notcompletely eliminate the distortions.

The method described herein determines the uncertainty associated withthe phase unwrapping solution and uses that to adaptively drive asubsequent image registration step to further improve the results inareas of high uncertainty. The method described herein can be used withregularized field map estimation techniques to account for noise inphase maps in regions of low spin density.

The method described herein can also utilize the fact that field mapstend to be piecewise smooth. (Some known techniques smooth the field mapusing a low pass filter; however, this can further propagate the errors,especially when the measured phase is severely corrupted). The methoddescribed herein can also combine the susceptibility correction withcorrection of other MRI artefacts arising from eddy currents andvibration.

The method described herein uses min-marginals as set out above tocharacterize uncertainty in the phase unwrapping solution. However,these are not exact marginal probabilities. Therefore, an inferencescheme such as a Markov Chain Monte Carlo, or approximate ones likeVariational Bayes or Expectation Propagation, could be used tocharacterize the posterior distributions and generate exact marginalprobabilities. However, such techniques could not be completed withinthe time constraints of a neurosurgical procedure for current computingresources.

The accurate measurement of phase is critical in various other MRIcontexts, such as flow imaging and susceptibility weighted imaging(SWI). SWI exploits the magnetic susceptibility differences betweenvarious tissues and the phase images generated from SWI are useful forthe detection of cerebral microbleeds in patients with traumatic braininjuries [36]. SWI requires long echo times and suffers from severephase wraps, especially in regions of sharp tissue susceptibilitydifferences. This phase unwrapping algorithm described herein is fastenough to be implemented directly in the scanner acquisition and imagecreation pipeline.

In summary, the approach described herein allows field map and imageregistration based correction approaches to be combined by estimatingthe uncertainty associated with the phase unwrapping step andincorporating it in the subsequent registration step. Such an approachhas been found to perform better than using field maps or imageregistration based techniques alone for interventionally acquired EPIimages on a dataset of thirteen subjects. This approach could be ofsignificant utility in image guided interventions and facilitateeffective neurosurgical treatments.

Although various embodiments of the invention have been describedherein, it will be appreciated that these are by way of example, andthat the details of any given implementation will vary according to theparticular requirements and circumstances of that implementation.Moreover, the skilled person will recognise that features from thedifferent embodiments described herein may be combined with otherembodiments as appropriate. Accordingly, the scope of the presentinvention is defined by the appended claims and their equivalents.

REFERENCES

-   [1] P. Daga, G. Winston, M. Modat, M. White, L. Mancini, M.    Cardoso, M. Symms, J. Stretton, A. McEvoy, J. Thornton, C.    Micallef, T. Yousry, D. Hawkes, J. Duncan, and S. Ourselin,    “Accurate localization of optic radiation during neurosurgery in an    interventional MRI suite,” Medical Imaging, IEEE Transactions on,    vol. 31, no. 4, pp. 882-891, April 2012.-   [2] G. P. Winston, P. Daga, J. Stretton, M. Modat, M. R.    Symms, A. W. McEvoy, S. Ourselin, and J. S. Duncan, “Optic radiation    tractography and vision in anterior temporal lobe resection,” Annals    of Neurology, vol. 71, no. 3, pp. 334-341, 2012.-   [3] X. Chen, D. Weigel, O. Ganslandt, M. Buchfelder, and C. Nimsky,    “Prediction of visual field deficits by diffusion tensor imaging in    temporal lobe epilepsy surgery,” Neurolmage, vol. 45, no. 2, pp.    286-297, 2009.-   [4] G. Sun, X. lei Chen, Y. Zhao, F. Wang, Z.-J. Song, Y. bo    Wang, D. Wang, and B. nan Xu, “Intraoperative MRI with integrated    functional neuronavigation-guided resection of supratentorial    cavernous malformations in eloquent brain areas,” Journal of    Clinical Neuroscience, vol. 18, no. 10, pp. 1350-1354, 2011.-   [5] G. Andrea, A. Angelini, A. Romano, A. Di Lauro, G. Sessa, A.    Bozzao, and L. Ferrante, “Intraoperative dti and brain mapping for    surgery of neoplasm of the motor cortex and the corticospinal tract:    our protocol and series in brainsuite,” Neurosurgical Review, vol.    35, pp. 401-412, 2012.-   [6] P. Jezzard and R. Balaban, “Correction for geometric distortion    in echo planar images from B0 field variations,” Magnetic Resonance    in Medicine, vol. 34, no. 1, pp. 65-73, 1995.-   [7] A. Funai, J. Fessler, D. Yeo, V. Olafsson, and D. Noll,    “Regularized field map estimation in MRI,” IEEE Transactions on    Medical Imaging, vol. 27, no. 10, pp. 1484-1494, 2008.-   [8] M. Jenkinson, “Fast, automated, n-dimensional phase-unwrapping    algorithm,” Magnetic Resonance in Medicine, vol. 49, no. 1, pp.    193-197, 2003.-   [9] M. Wu, L. Chang, L. Walker, H. Lemaitre, A. Barnett, S. Marenco,    and C. Pierpaoli, “Comparison of EPI distortion correction methods    in diffusion tensor MRI using a novel framework,” in MICCAI, 2008,    pp. 321-329.-   [10] J. Kybic, P. Thevenaz, A. Nirkko, and M. Unser, “Unwarping of    unidirectionally distorted EPI images,” IEEE Transactions on Medical    Imaging, vol. 19, no. 2, pp. 80-93, 2000.-   [11] D. Merhof, G. Soza, A. Stadlbauer, G. Greiner, and C. Nimsky,    “Correction of susceptibility artifacts in diffusion tensor data    using non-linear registration,” Medical Image Analysis, vol. 11, no.    6, pp. 588-603, 2007.-   [12] R. Tao, P. Fletcher, S. Gerber, and T. Whitaker, “A variational    image based approach to the correction of susceptibility artifacts    in the alignment of diffusion weighted and structural MRI,” in IPMI,    2009, pp. 664-675.-   [13] H. Huang, C. Ceritoglu, X. Li, A. Qiu, M. I. Miller, P. C. van    Zijl, and S. Mori, “Correction of B0 susceptibility induced    distortion in diffusion-weighted images using large-deformation    diffeomorphic metric mapping,” Magnetic Resonance Imaging, vol. 26,    no. 9, pp. 1294-1302, 2008.-   [14] M. O. Irfanoglu, L. Walker, S. Sammet, C. Pierpaoli, and R.    Machiraju, “Susceptibility distortion correction for echo planar    images with non-uniform b-spline grid sampling: a diffusion tensor    image study,” in MICCAI, 2011, pp. 174-181.-   [15] M. Jenkinson, J. L. Wilson, and P. Jezzard, “Perturbation    method for magnetic field calculations of nonconductive objects,”    Magnetic Resonance in Medicine, vol. 52, no. 3, 2004.-   [16] C. Studholme, R. Constable, and J. Duncan, “Accurate alignment    of functional EPI data to anatomical MRI using a physics-based    distortion model,” Medical Imaging, IEEE Transactions on, vol. 19,    no. 11, pp. 1115-1127, nov. 2000.-   [17] L. Ruthotto, H. Kugel, J. Olesch, B. Fischer, J.    Modersitzki, M. Burger, and C. H. Wolters, “Diffeomorphic    susceptibility artifact correction of diffusion-weighted magnetic    resonance images,” Physics in Medicine and Biology, vol. 57, no.    18, p. 5715, 2012.-   [18] L. Ying, Z.-P. Liang, J. Munson, D. C., R. Koetter, and B.    Frey, “Unwrapping of MR phase images using a markov random field    model,” Medical Imaging, IEEE Transactions on, pp. 128-136, 2006.-   [19] S. Z. Li, “Markov random field models in computer vision,” in    Proceedings of the third European conference on Computer Vision    (Vol. II), 1994, pp. 361-370.-   [20] Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy    minimization via graph cuts,” IEEE Transactions on Pattern Analysis    and Machine Intelligence, vol. 23, p. 2001, 2001.-   [21] V. Kolmogorov and R. Zabin, “What energy functions can be    minimized via graph cuts?” PAMI, IEEE Transactions on, pp. 147-159,    2004.-   [22] L. R. Ford and D. R. Fulkerson, Flows in Networks. Princeton    University Press, 1962.-   [23] D. Tisdall and M. S. Atkins, “MRI denoising via phase error    estimation,” in in Proc. SPIE, 5747, 2005, pp. 646-654.-   [24] P. Kohli and P. H. S. Torr, “Measuring uncertainty in graph cut    solutions,” Comput. Vis. Image Underst., pp. 30-38, 2008.-   [25] B. Glocker, N. Komodakis, G. Tziritas, N. Navab, and N.    Paragios, “Dense image registration through MRFs and efficient    linear programming,” Medical Image Analysis, pp. 731-741, 2008.-   [26] R. W. K. So, T. W. H. Tang, and A. C. S. Chung, “Non-rigid    image registration of brain magnetic resonance images using    graph-cuts,” Pattern Recogn., vol. 44, no. 10-11, October 2011.-   [27] D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D.    Hawkes, “Nonrigid registration using free-form deformations:    Application to breast MR images,” Medical Imaging, IEEE Transactions    on, vol. 18, no. 8, pp. 712-721, 1999.-   [28] X. Zhuang, S. Arridge, D. Hawkes, and S. Ourselin, “A nonrigid    registration framework using spatially encoded mutual information    and free-form deformations,” Medical Imaging, IEEE Transactions on,    pp. 1819-1828, 2011.-   [29] C. Studholme, H. D., and H. D., “An overlap invariant entropy    measure of 3d medical image alignment,” Pattern Recognition, vol.    32, no. 1, pp. 71-86, January 1999.-   [30] I. Drobnjak, D. Gavaghan, E. Sli, J. Pitt-Francis, and M.    Jenkinson, “Development of a functional magnetic resonance imaging    simulator for modeling realistic rigid-body motion artifacts,”    Magnetic Resonance in Medicine, vol. 56, pp. 364-80, 2006.-   [31] I. Drobnjak, G. Pell, and M. Jenkinson, “Simulating the effects    of time-varying magnetic fields with a realistic simulated scanner,”    Magnetic Resonance Imaging, vol. 28, no. 7, pp. 1014-21, September    2010.-   [32] D. Collins, A. Zijdenbos, V. Kollokian, J. Sled, N. Kabani, C.    Holmes, and A. Evans, “Design and construction of a realistic    digital brain phantom,” Medical Imaging, IEEE Transactions on, vol.    17, no. 3, pp. 463-468, june 1998.-   [33] S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F.    Beckmann, T. E. Behrens, H. Johansen-Berg, P. R. Bannister, M. D.    Luca, I. Drobnjak, D. E. Flitney, R. K. Niazy, J. Saunders, J.    Vickers, Y. Zhang, N. D. Stefano, J. M. Brady, and P. M. Matthews,    “Advances in functional and structural MR image analysis and    implementation as FSL,” Neurolmage, vol. 23, Supplement 1, no. 0,    pp. S208-S219, 2004.-   [34] K. P. Pruessmann, “Encoding and reconstruction in parallel    mri,” NMR in Biomedicine, vol. 19, no. 3, pp. 288-299, 2006.-   [35] D. Atkinson, D. A. Porter, D. L. Hill, F. Calamante, and A.    Connelly, “Sampling and reconstruction effects due to motion in    diffusion-weighted interleaved echo planar imaging,” Magnetic    Resonance in Medicine, vol. 44, no. 1, pp. 101-109, 2000.-   [36] E. Haacke, S. Mittal, Z. Wu, J. Neelavalli, and Y.-C. Cheng,    “Susceptibility-weighted imaging: Technical aspects and clinical    applications, part 1,” American Journal of Neuroradiology, vol. 30,    no. 1, pp. 19-30, January 2009.

What is claimed is:
 1. A method of correcting susceptibility artefactsin an acquired magnetic resonance image comprising the steps of:performing phase unwrapping for an acquired magnetic resonance (MR)image, including computing a confidence for the phase unwrapping foreach voxel of the acquired MR image; generating a field map for theacquired magnetic resonance image from the unwrapped phase; convertingthe field map to a deformation field; and using the deformation field toinitialise a non-rigid image registration of the acquired MR imageagainst a reference image, wherein the deformation field of thenon-rigid registration is controlled to be smoother where saidconfidence is high.
 2. The method of claim 1, wherein performing thephase unwrapping for an acquired magnetic resonance (MR) imagecomprises: modelling the MR phase in the MR image using a Markov randomfield (MRF) in which the true phase φ(t) and the wrapped phase φ(w) aremodelled as random variables such that at voxel i of said MR imageφ(t)(i)=φ(w)(i)+2πn(i), where n(i) is an unknown integer that needs tobe estimated for each voxel i; constructing a graph consisting of a setof vertices V and edges E and two special terminal vertices representinga source s and sink t, where there is a one-to-one correspondencebetween cuts on the graph and configurations of the MRF, a cutrepresenting a partition of the vertices V into disjoint sets S and Tsuch that sεS and tεT; finding the minimum energy configuration,E(n(i)|φ(w)) of the MRF on the basis that the total cost of a given cutrepresents the energy of the corresponding MRF configuration, where thecost of a cut is the sum of all edges going from S to T across the cutboundary; and using the values of n(i) in the minimum energyconfiguration to perform the phase unwrapping from φ(w) to φ(t) for theMR image.
 3. The method of claim 2, wherein the MR phase is modelled asa piecewise smooth function where the smooth component is due to theinhomogeneities in the static MR field and the non-smooth componentarises due to changes in magnetic susceptibility from one tissue type toanother, and wherein the spatial smoothness is enforced by modelling thetrue phase as the MRF and a smoothness model is incorporated by using apotential function.
 4. The method of claim 3, wherein the true phase ismodelled as a six-neighbourhood pairwise MRF where a convex pairwisepotential function is defined between immediately adjacent neighbouringvoxels.
 5. The method of claim 4, wherein the convex pairwise potentialfunction is sum of squared differences.
 6. The method of claim 2,further comprising weighting voxel contributions on the basis that phasemeasurements in low signal areas of the MR image tend to be lessreliable, such that phase measurements in high signal areas of the MRimage are assigned a higher weight that phase measurements in low signalareas of the MR image.
 7. The method of claim 6, wherein the weightassigned to a given voxel corresponds to the probability that thecontribution of noise to the phase of a given voxel is less than cradians, where c is set to be a small value greater than zero.
 8. Themethod of claim 7, further comprising selecting a region in the MR imagecontaining only air to determine the noise variance of the MR image,wherein said noise variance is then used for determining the weight tobe assigned to a given voxel.
 9. The method of claim 2, whereincomputing a confidence comprises performing a confidence estimation ateach voxel for the phase unwrapping.
 10. The method of claim 9, whereinthe confidence associated with a given label for a random variable isestimated as the ratio of the min-marginal energy associated with thatlabelling to the sum of the min-marginal energies for that randomvariable for all possible labels, wherein the min-marginal is theminimum energy obtained when a random variable is constrained to take acertain label and the minimisation is performed over all remainingvariables.
 11. The method of claim 10, wherein the confidence iscomputed for each voxel using dynamic graph cuts.
 12. The method ofclaim 11, wherein using dynamic graph cuts comprises constraining agiven MRF node to belong to the source or sink by adding an infinitecapacity edge between the given MRF node and the respective terminalnode, and computing the min-marginal energies by optimizing theresulting MRF.
 13. The method of claim 1, wherein the confidence iscomputed for each voxel using one of the following techniques:Expectation Propagation, Variational Bayes, Markov Chain Monte Carlo(MCMC) and Hamiltonian MCMC simulations.
 14. The method of claim 1,wherein generating the field map comprises using the unwrapped phasefrom two MR images or the unwrapped phase difference between the two MRimages to estimate a field map.
 15. The method of claim 14, furthercomprising converting the field map into a deformation field.
 16. Themethod of claim 15, wherein the deformation field is used to initialisea non-rigid image registration of the acquired MR image against areference image.
 17. The method of claim 16, wherein the deformationfield for performing the non-rigid image registration is parameterisedusing cubic B-splines, where the B-splines are constrained to move onlyin the phase encode direction.
 18. The method of claim 17, wherein asimilarity measure used for the non-rigid image registration is based onlocal information which encodes spatial context by varying thecontribution of voxels according to their spatial coordinates.
 19. Themethod of claim 18, wherein the non-rigid image registration isrepresented as a discrete set of displacements in the phase encodedirection, and the non-rigid image registration is modelled as afirst-order Markov random field.
 20. The method of claim 19, wherein thefirst derivative of the deformation field for the non-rigid imageregistration is penalised to ensure a smooth transformation.
 21. Themethod of claim 20, wherein a penalty term for penalising the firstderivative of the deformation field is modulated by the confidencecomputed for each voxel, such that the penalty weight is higher inregions of high confidence than in regions of low confidence.
 22. Themethod of claim 1, wherein a confidence for each voxel of the field mapis determined from the computed confidence for the phase unwrapping andan estimate of uncertainty in the MR image, and wherein the confidenceof the field map is used to control the smoothness of the deformationfield.
 23. A method of performing phase unwrapping for an acquiredmagnetic resonance (MR) image comprising: modelling the MR phase in theMR image using a Markov random field (MRF) in which the true phase φ(t)and the wrapped phase φ(w) are modelled as random variables such that atvoxel i of said MR image φ(t)(i)=φ(w)(i)+2πn(i), where n(i) is anunknown integer that needs to be estimated for each voxel i;constructing a graph consisting of a set of vertices V and edges E andtwo special terminal vertices representing a source s and sink t, wherethere is a one-to-one correspondence between cuts on the graph andconfigurations of the MRF, a cut representing a partition of thevertices V into disjoint sets S and T such that sεS and tεT; finding theminimum energy configuration, E(n(i)|φ(w)) of the MRF on the basis thatthe total cost of a given cut represents the energy of the correspondingMRF configuration, where the cost of a cut is the sum of all edges goingfrom S to T across the cut boundary; using the values of n(i) in theminimum energy configuration to perform the phase unwrapping from φ(w)to φ(t) for the MR image; and performing a confidence estimation at eachvoxel for the phase unwrapping.
 24. The method of claim 23, wherein theMR phase is modelled as a piecewise smooth function where the smoothcomponent is due to the inhomogeneities in the static MR field and thenon-smooth component arises due to changes in magnetic susceptibilityfrom one tissue type to another, and wherein the spatial smoothness isenforced by modelling the true phase as the MRF and a smoothness modelis incorporated by using a potential function.
 25. The method of claim24, wherein the true phase is modelled as a six-neighbourhood pairwiseMRF where a convex pairwise potential function is defined betweenimmediately adjacent neighbouring voxels.
 26. The method of claim 25,wherein the convex pairwise potential function is sum of squareddifferences.
 27. The method of claim 23, further comprising weightingvoxel contributions on the basis that phase measurements in low signalareas of the MR image tend to be less reliable, such that phasemeasurements in high signal areas of the MR image are assigned a higherweight that phase measurements in low signal areas of the MR image. 28.The method of claim 27, wherein the weight assigned to a given voxelcorresponds to the probability that the contribution of noise to thephase of a given voxel is less than c radians, where c is set to be asmall value greater than zero.
 29. The method of claim 28, furthercomprising selecting a region in the MR image containing only air todetermine the noise variance of the MR image, wherein said noisevariance is then used for determining the weight to be assigned to agiven voxel.
 30. The method of claim 22, wherein the confidenceassociated with a given label for a random variable is estimated as theratio of the min-marginal energy associated with that labelling to thesum of the min-marginal energies for that random variable for allpossible labels, wherein the min-marginal is the minimum energy obtainedwhen a random variable is constrained to take a certain label and theminimisation is performed over all remaining variables.
 31. The methodof claim 29, wherein the confidence is computed for each voxel usingdynamic graph cuts.
 32. The method of claim 31, wherein using dynamicgraph cuts comprises constraining a given MRF node to belong to thesource or sink by adding an infinite capacity edge between the given MRFnode and the respective terminal node, and computing the min-marginalenergies by optimizing the resulting MRF.
 33. The method of claim 23,wherein generating the field map comprises using the unwrapped phasefrom two MR images or the unwrapped phase difference between the two MRimages to estimate a field map.
 34. The method of claim 33, furthercomprising converting the field map into a deformation field.
 35. Themethod of claim 34, wherein the deformation field is used to initialisea non-rigid image registration of the acquired MR image against areference image.
 36. The method of claim 35, wherein the deformationfield for performing the non-rigid image registration is parameterisedusing cubic B-splines, where the B-splines are constrained to move onlyin the phase encode direction.
 37. The method of claim 36, wherein asimilarity measure used for the non-rigid image registration is based onlocal information which encodes spatial context by varying thecontribution of voxels according to their spatial coordinates.
 38. Themethod of claim 37, wherein the non-rigid image registration isrepresented as a discrete set of displacements in the phase encodedirection, and the non-rigid image registration is modelled as afirst-order Markov random field.
 39. The method of claim 38, wherein thefirst derivative of the deformation field for the non-rigid imageregistration is penalised to ensure a smooth transformation.
 40. Themethod of claim 39, wherein a penalty term for penalising the firstderivative of the deformation field is modulated by the confidencecomputed for each voxel, such that the penalty weight is higher inregions of high confidence than in regions of low confidence. 41.(canceled)
 42. (canceled)
 43. (canceled)
 44. (canceled)
 45. Apparatusfor correcting susceptibility artefacts in an acquired magneticresonance image, the apparatus being configured to perform theoperations of: performing phase unwrapping for an acquired magneticresonance (MR) image, including computing a confidence for the phaseunwrapping for each voxel of the acquired MR image; generating a fieldmap for the acquired magnetic resonance image from the unwrapped phase;converting the field map to a deformation field; and using thedeformation field to initialise a non-rigid image registration of theacquired MR image against a reference image, wherein the deformationfield of the non-rigid registration is controlled to be smoother wheresaid confidence is high.
 46. The apparatus of claim 45, wherein saidapparatus is an MR system for supporting image-guided surgery.
 47. Anon-transitory computer-readable storage medium storing instructionsthat when executed by a computer cause the computer to perform a methodfor correcting susceptibility artefacts in an acquired magneticresonance image, the method comprising: performing phase unwrapping foran acquired magnetic resonance (MR) image, including computing aconfidence for the phase unwrapping for each voxel of the acquired MRimage; generating a field map for the acquired magnetic resonance imagefrom the unwrapped phase; converting the field map to a deformationfield; and using the deformation field to initialise a non-rigid imageregistration of the acquired MR image against a reference image, whereinthe deformation field of the non-rigid registration is controlled to besmoother where said confidence is high.